Instructions to use dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "dzanbek/9c0ba83d-7ad1-4ee2-9ce3-e505d153c9e6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41a5a508ac15e36aa64d0d4716ac1f75fadfc1d2c4aadb38b6c086911ab89bc9
- Size of remote file:
- 6.78 kB
- SHA256:
- 739ccea0a7e79665f33b1eb96fb953801aa4a3a7b841a784fec17cf03463e1c9
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